Rising concern over the degradation of the environment, such as erosion and sediment loads, warrants the integration of the complex and dispersed geographical data sets. This paper describes the use of Geographic Information System (GIS) and remote sensing for assessing the impact of land use changes to water turbidity in multiple watersheds. In this study, necessary data sets representing land uses, hydrology, weather, soils, elevation, and surface characteristics were integrated in a GIS in tabular, vector and grid formats. The land use maps that were derived from Landsat-5 TM imagery using a combination of different classification strategies gave an average accuracy of 95 %. Results from data analysis had shown that there exists a close relationship existed between the extent of open area and sedimentation loading rate. However, the sediment loading rates were found to be non-linear ranging from 1.47 to 2.13 tonnes per millimeter of rainfall for each kilometer-square increase of open areas, depending on their location of open areas with respect to factors such as availability of sediment, soil type, slope length, and slope steepness etc.

INTRODUCTION

In recent years, Malaysia has undergone very rapid development with subsequent population growth, urbanisation, industrialisation, logging activities, and expansion of agricultural areas. These changes have caused complex environmental problems and the most affected natural resources is water. Inherent in the solution to the above problem and many environmental problems is the need to bring together dispersed geographical data sets. The complexity and size of these databases make the requirement for application of Geographical Information System (GIS) and remote sensing technology all the more necessary. By bringing key data and analytical components together under a GIS environment, the problems of lack of integration, limited coordination, and time-intensive execution typical of the more traditional assessment tools faced by most users can be overcome.

GROUND COVER CLASSIFICATION

The overall objective of image classification procedures is to systematically categorise all pixels in an image into land cover classes. Pattern recognition techniques to classify ground cover characteristics through remotely sensed data have been used in many hydrologic modeling studies [1, 2, 11, 13, 14, and 15]. Multi-spectral are used to perform the classification, and indeed, the spectral pattern within the data for each pixel is used as the numerical basis for categorisation. Spectral pattern recognition classification is the process of sorting pixels into a finite number of individual classes. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to that criteria. Multi-spectral classification can be performed by supervised or unsupervised method based on multivariate statistical criteria. This procedure assumes that imagery of a specific area is
collected in multiple regions of the electromagnetic spectrum (e.g. seven bands of TM data) and that the images are in good registration [16].

DESCRIPTION OF THE STUDY AREA

The Upper Bernam Basin (UBB) had been selected for this study. It had been identified as the ultimate and largest source of water supply for the Bernam Valley especially for irrigation. The river basin covers an area of 1090 km2 with SKC Bridge river monitoring station as the downstream outlet. The SKC Bridge monitoring station is 17 km upstream of Bernam River Headwork (BRH) and 147 km upstream of the estuary of the Bernam River. A brief description of the river basin is summarised in Table 1. Figure 1 shows the locality of the study area.

GIS AND REMOTE SENSING APPLICATION

The topographic and soil maps (scale 1:50000) were used to produce all the digital thematic layers, grid themes, digital elevation model (DEM), etc, to provide physiological and geographical information. Based on the information, the study area was delineated as shown in Figure 2, where it can be seen that the Upper Bernam river basin has diverse relief. About 80% of the basin is steep mountainous region rising to a height of 1830 m in the northern and eastern direction. The remaining area at the central and downstream part is hilly land with swamps.
The soil information for the study area was digitized as shown in Figure 3. Generally, eight (8) types of soil had been identified in the study area. At the upper main range, which consists of steep land, perennial granite predominantly coarse-grained mega crystic granite is found. In the upper reach area, the soils consist mainly of Rengam and Munchong-Seremban series. These sedimentation soils contain generally fine to coarse quartz sand set in a clay matrix. The lower areas consist of Serdang series, which is more sandy and thicker. Local Alluvium-Colluvium and soils derived from riverine alluvium such as Akob and Merbau Patah series also can be found along the river bed. Most of the soils mentioned above are well drained. Textural classes mostly lie between loam to clay with moderate to average soil moisture holding capacity.

CONCLUSIONS

In order to assess the classification accuracy, 250 points were generated randomly throughout each image using the Add Random Point utility. A class value was then entered for each of these points, which would be the reference points used to compare to the class values of the classified image. From this study, land use maps with an average accuracy of 95% had been derived from the Landsat TM image classification. Necessary digital maps had been overlaid against each other successfully and all the necessary data were well managed under a GIS environment. Results from the data analysis had shown that there is a non-linear relationship between open areas and sedimentation loading rate.

Growing concern about environmental degradation, such as erosion and sediment loads, justifies the integration of complex and dispersed geographic data sets. This paper describes the use of the Geographic Information System (GIS) and remote sensing to assess the impact of land use changes on water turbidity in multiple watersheds. In this study, the necessary datasets representing land uses, hydrology, climate, soils, elevation and surface characteristics were integrated into a GIS in tabular, vector, and grid format. The land use maps derived from the Landsat-5 TM images using a combination of different classification strategies gave an average accuracy of 95%. The results of the data analysis had shown that there is a close relationship between the extent of the open area and the rate of sedimentation load. However, sediment load rates were not linear, ranging from 1.47 to 2.13 tonnes per millimeter of rain per square kilometer of open areas, depending on the location of open areas with respect to factors such as sediment availability, soil type, slope, slope, etc.

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